知识管理
探索性搜索
匹配(统计)
过程(计算)
桥(图论)
透视图(图形)
计算机科学
拆箱
跟踪(心理语言学)
探索性研究
开放式创新
钥匙(锁)
数据科学
组织学习
偶然性
探索性分析
创新管理
新兴技术
新产品开发
业务
新兴市场
知识抽取
产品(数学)
知识经济
最佳实践
知识获取
作者
Kathrin Reinsberger,Vegard Kolbjørnsrud,Barbara Mehner
出处
期刊:Research Policy
[Elsevier BV]
日期:2025-10-21
卷期号:55 (1): 105348-105348
被引量:1
标识
DOI:10.1016/j.respol.2025.105348
摘要
Innovation involves matching needs and solutions to form need–solution pairs (NSPs). This study investigates how organizations systemically identify and develop NSPs when searching for novel applications of existing technologies through technology-market linking—a critical yet underexplored process in strategy and innovation research. Using a multiple case study design, we analyze four innovation projects drawing on 306 expert interviews, 89 innovation proposals, and longitudinal data from diaries and retrospective interviews with 18 search agents. Our findings show that searchers engage in recurring learning practices to acquire knowledge about unmet needs, potential solutions, and how they co-evolve. These practices structure the integration of different types of knowledge over time, giving rise to four distinct search patterns that guide the direction and evolution of innovation efforts. With this study, we advance research on problem solving in innovation by unpacking how NSPs can be deliberately discovered and developed through exploratory search, rather than emerging solely from spontaneous or serendipitous encounters. We expand the literature on organizational search and learning by empirically documenting the micro-level learning processes and behaviors enabling the dynamic coupling of need and solution spaces. Finally, we contribute to the open innovation perspective by demonstrating how external knowledge critically shapes emerging technology–market combinations. • Address the challenge of matching technologies to market needs. • Analyze technology–market linking as problem solving via need–solution pairs (NSPs). • Trace how deliberate exploratory search drives co-evolution of needs and solutions. • Identify key learning practices and search patterns driving innovation efforts. • Bridge serendipitous and deliberate discovery, guiding systematic NSP development.
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